DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images

Abstract

We present DLTK, a toolkit providing baseline implementations for efficient experimentation with deep learning methods on biomedical images. It builds on top of TensorFlow and its high modularity and easy-to-use examples allow for a low-threshold access to state-of-the-art implementations for typical medical imaging problems. A comparison of DLTK's reference implementations of popular network architectures for image segmentation demonstrates new top performance on the publicly available challenge data "Multi-Atlas Labeling Beyond the Cranial Vault". The average test Dice similarity coefficient of $81.5$ exceeds the previously best performing CNN ($75.7$) and the accuracy of the challenge winning method ($79.0$).

arXiv commentSubmitted to Medical Imaging Meets NIPS 2017, Code at https://github.com/DLTK/DLTK

References in corpus (3)

• Adam: A Method for Stochastic Optimization
• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
• NiftyNet: a deep-learning platform for medical imaging

Cited by in corpus (16)

• TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning
• PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation
• Interactive segmentation of medical images through fully convolutional neural networks
• medigan: a Python library of pretrained generative models for medical image synthesis
• Prior-aware Neural Network for Partially-Supervised Multi-Organ Segmentation
• pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis
• GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging
• SenseCare: A Research Platform for Medical Image Informatics and Interactive 3D Visualization
• Fully Automated Segmentation of Hyperreflective Foci in Optical Coherence Tomography Images
• DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets
• Ivy: Templated Deep Learning for Inter-Framework Portability
• Active Learning for Breast Cancer Identification
• Small Organ Segmentation in Whole-body MRI using a Two-stage FCN and Weighting Schemes
• Representation Disentanglement for Multi-task Learning with application to Fetal Ultrasound
• Eisen: a python package for solid deep learning
• Semantic-Aware Contrastive Learning for Multi-object Medical Image Segmentation